Computational forecasting systems are important and widely used tools in finance, business, commerce, governmental agencies, research organizations, and other institutions. There are myriad different reasons why organizations need to predict, as accurately as possible, future trends and events. As one example, a construction firm may need to predict, well in advance of undertaking actual construction, future demand for new housing and commercial buildings in order to acquire necessary employees, find and acquire property, undertake necessary license applications, and arrange for acquisition of needed supplies and materials. Although undertaking such activities can be based on intuitive guesses with regard to general economic trends, such intuitive guesses are often inaccurate, leading to unnecessarily large supplies-and-materials inventories and overstaffing, when the guesses prove to have overestimated demand, and inefficient and expensive last-minute hiring and deal making, when the guesses have underestimated demand. In both cases, the expenses incurred by the construction company may significantly exceed optimal expenses that would have been obtained with accurate prediction of construction demand.
Many different types of forecasting systems and methods have been developed, over the years, including highly complex and sophisticated financial forecasting systems, business demand forecasting systems, and many other computational forecasting methods and systems. While current methods appear to have justified the expenses incurred in developing and purchasing them, there remains a great demand in many of the above-mentioned types of organizations for new and different computational forecasting methods and systems.